Interval forecasting of renewable power generation
With the development of renewable energy power generation industry, effective prediction of renewable energy generation is an important issue that modern power grids are facing. Solar power generation is an important part of renewable energy generation. In this project, solar incident radiation (...
Saved in:
Main Author: | |
---|---|
Other Authors: | |
Format: | Theses and Dissertations |
Language: | English |
Published: |
2019
|
Subjects: | |
Online Access: | http://hdl.handle.net/10356/78411 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-78411 |
---|---|
record_format |
dspace |
spelling |
sg-ntu-dr.10356-784112023-07-04T16:18:18Z Interval forecasting of renewable power generation Luo, Lingfeng Xu Yan School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering::Electric power::Production, transmission and distribution With the development of renewable energy power generation industry, effective prediction of renewable energy generation is an important issue that modern power grids are facing. Solar power generation is an important part of renewable energy generation. In this project, solar incident radiation (SIR) is used as training and test data for research. By using long short term memory (LSTM) to train network parameters, the results of point forecasting are obtained which is then converted into prediction intervals by quantile regression method. Considering the uncertainty of SIR, the prediction interval is more effective than the conventional point forecasting results. Various LSTM framings are used in this project for comparison and analysis. The conclusions have a guiding role in solar power generation prediction Master of Science (Power Engineering) 2019-06-19T13:00:03Z 2019-06-19T13:00:03Z 2019 Thesis http://hdl.handle.net/10356/78411 en 58 p. application/pdf |
institution |
Nanyang Technological University |
building |
NTU Library |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
NTU Library |
collection |
DR-NTU |
language |
English |
topic |
DRNTU::Engineering::Electrical and electronic engineering::Electric power::Production, transmission and distribution |
spellingShingle |
DRNTU::Engineering::Electrical and electronic engineering::Electric power::Production, transmission and distribution Luo, Lingfeng Interval forecasting of renewable power generation |
description |
With the development of renewable energy power generation industry, effective
prediction of renewable energy generation is an important issue that modern power grids
are facing. Solar power generation is an important part of renewable energy generation.
In this project, solar incident radiation (SIR) is used as training and test data for research.
By using long short term memory (LSTM) to train network parameters, the results of
point forecasting are obtained which is then converted into prediction intervals by quantile regression method. Considering the uncertainty of SIR, the prediction interval is
more effective than the conventional point forecasting results. Various LSTM framings
are used in this project for comparison and analysis. The conclusions have a guiding role
in solar power generation prediction |
author2 |
Xu Yan |
author_facet |
Xu Yan Luo, Lingfeng |
format |
Theses and Dissertations |
author |
Luo, Lingfeng |
author_sort |
Luo, Lingfeng |
title |
Interval forecasting of renewable power generation |
title_short |
Interval forecasting of renewable power generation |
title_full |
Interval forecasting of renewable power generation |
title_fullStr |
Interval forecasting of renewable power generation |
title_full_unstemmed |
Interval forecasting of renewable power generation |
title_sort |
interval forecasting of renewable power generation |
publishDate |
2019 |
url |
http://hdl.handle.net/10356/78411 |
_version_ |
1772826872787238912 |